At-fault Crashes Research Articles

News coverage of older drivers' fatal car crashes: is it over-represented?

Japan's stringent licensing policies for older drivers have not been questioned, possibly due to negative perceptions of older drivers potentially influenced by media coverage of their car crashes. We examined whether older drivers' fatal crashes are over-represented in the news articles. To examine the news coverage of fatal crashes that occurred between January 2016 and December 2020, we extracted driver- and crash-related data from articles reporting fatal crashes in the two best-selling newspapers, Yomiuri and Asahi. We obtained the corresponding data of police-reported fatal crashes during the same period. We calculated the proportion of newspaper-reported fatal crashes to police-reported fatal crashes by at-fault driver's age group and crash characteristics. Of 12,987 police-reported fatal crashes, 5,888 (45%) and 2,909 (22%) crashes were reported in Yomiuri and Asahi newspapers, respectively. Excluding 2,098 crashes where at-fault drivers or their ages were not identifiable, Yomiuri reported 39%, 35%, and 31%, and Asahi reported 20%, 16%, and 14% of fatal crashes caused by drivers aged <30 years, 30-69 years, and 70 years or older, respectively. Crashes that caused more fatalities or killed children tended to be reported regardless of at-fault drivers' age groups. Compared with young and middle-aged drivers, older drivers' fatal crashes involving child fatalities were more reported, whereas their single fatal crashes ending in their own deaths were less reported. Older drivers' at-fault fatal crashes were not over-represented in the news coverage of overall fatal crashes, and their crashes killing themselves were under-reported.

LOCATION MODERATES THE EFFECT OF COGNITIVE TRAINING ON CRASH RISK OVER 20 YEARS: AN ANALYSIS OF THE ACTIVE STUDY

Abstract Driving is a critical everyday function that helps older adults remain independent. The Advanced Cognitive Training in Vital Elderly (ACTIVE) study has shown cognitive training improves driving safety outcomes for older adults. Few studies have examined the longitudinal effects of cognitive training on crash risk beyond 5 years. To address this empirical gap, we aimed to determine the effects of cognitive training on crash risk over 20 years, and how urbanicity moderates these relationships. Using participants from the no-contact control and intervention (speed of processing, memory, and reasoning training) groups in the ACTIVE study (n=2170) and state-recorded crash reports, cox proportional hazards models were used to examine the effect of three cognitive training interventions as predictors of time to first at-fault crash over 20 years. Participants (mean age 73, 73% White, 23% Male) reported driving 90.6 miles/week on average and 12.6% experienced an at-fault crash. Overall, speed of processing training was the only intervention related to lower crash risk, but this varied by urbanicity. Namely, speed of processing training was related to lower crash risk for individuals living in rural environments (HR=.16, 95% CI [.04-.73], p=.018) but not for individuals in urban environments (HR=.89, 95% CI [.54-1.50], p=.679). Findings highlight the safety benefits of speed of processing training for older drivers living in rural environments where more fatal crashes occur. However, interventions are still needed to improve safety in urban environments where more crashes occur generally. Consideration of location in future interventions may help improve safe driving for older adults.

Trend in alcohol-related crashes before and after the introduction of mandatory breath testing among commercial truck drivers.

Since 2011, commercial truck drivers have been required to take alcohol breath tests at the beginning and end of their working hours due to their employers' legal obligations. However, non-commercial truck drivers are not required to do so. We examined whether alcohol-related crashes had decreased after 2011 among commercial truck drivers. Using police data, we conducted a joinpoint regression analysis to examine the trend in the proportion of alcohol-related crashes from 1995 to 2020 caused by commercial truck drivers (who were subjected to alcohol breath testing) and non-commercial truck drivers (who were not subjected to testing). The annual percentage change in this trend was also estimated. During the 26-year study period, truck drivers caused 1,846,321 at-fault crashes, and 0.4% of the crashes involved intoxicated driving. A significant decreasing trend in the proportion of alcohol-related crashes was identified among both commercial and non-commercial truck drivers in the 2000s, during which several legal amendments were made against drunk driving. The annual percentage change was -13.5% from 2001 to 2012 among commercial truck drivers, and -14.9% from 2001 to 2011 among non-commercial truck drivers. No decreasing trend was observed afterwards, despite the introduction of mandatory alcohol breath testing in 2011. The effect of mandatory alcohol breath testing on reducing alcohol-related crashes among commercial truck drivers was not evident.

Analysis of factors that influence injury severity of single and multivehicle crashes involving at-fault older drivers: A random parameters logit with heterogeneity in means and variances approach

The number of older drivers on our roads have been increasing given the upward shift of older people in the population. It is well-known that older drivers are more susceptible to injury given a crash. They are also likely to be at-fault in certain types of crashes due to health conditions associated with old age. This paper identifies factors that influence the severity of crashes involving at-fault older drivers at a disaggregated level for single and multivehicle crashes. Two random parameters multinomial logit with heterogeneity in means and variances models of injury severity were developed to assess the impact of various crash factors on crash outcomes based on the crash type. The study used five years of crash data from Alabama. The model estimations show that rainy weather and dark roadways were associated with an increased likelihood of serious injuries for single-vehicle crashes. Older male drivers were also found to have a higher probability of being at-fault in fatal single-vehicle crashes. For multivehicle older driver crashes, the model estimation results revealed that fatal injury was more likely to occur when the airbags were deployed. In addition, drivers between 65 and 75 years are less likely to be involved in fatal injury crashes for both single and multivehicle crashes. It was further found that older driver at-fault crashes that occurred in the open country were more likely to be fatal regardless of the manner of collision. The findings of this study are expected to help policymakers and transportation stakeholders to implement countermeasures that will make the road safer for older drivers and all other road users.

Risk Factors Influencing Fatal Powered Two-Wheeler At-Fault and Not-at-Fault Crashes: An Application of Spatio-Temporal Hotspot and Association Rule Mining Techniques

Studies have explored the factors influencing the safety of PTWs; however, very little has been carried out to comprehensively investigate the factors influencing fatal PTW crashes while considering the fault status of the rider in crash hotspot areas. This study employs spatio-temporal hotspot analysis and association rule mining techniques to discover hidden associations between crash risk factors that lead to fatal PTW crashes considering the fault status of the rider at statistically significant PTW crash hotspots in South Korea from 2012 to 2017. The results indicate the presence of consecutively fatal PTW crash hotspots concentrated within Korea’s densely populated capital, Seoul, and new hotspots near its periphery. According to the results, violations such as over-speeding and red-light running were critical contributory factors influencing PTW crashes at hotspots during summer and at intersections. Interestingly, while reckless riding was the main traffic violation leading to PTW rider at-fault crashes at hotspots, violations such as improper safety distance and red-light running were strongly associated with PTW rider not-at-fault crashes at hotspots. In addition, while PTW rider at-fault crashes are likely to occur during summer, PTW rider not-at-fault crashes mostly occur during spring. The findings could be used for developing targeted policies for improving PTW safety at hotspots.

Older driver at-fault crashes at unsignalized intersections in Alabama: Injury severity analysis with supporting evidence from a deep learning based approach

Introduction: The research described in this paper explored the factors contributing to the injury severity resulting from the male and female older driver (65 years and older) at-fault crashes at unsignalized intersections in Alabama. Method: Random parameter logit models of injury severity were estimated. The estimated models identified a variety of statistically significant factors influencing the injury severities resulting from older driver at-fault crashes. Results: According to these models, some variables were found to be significant only in one model (male or female) but not in the other one. For example, variables such as driver under the influence of alcohol/drugs, horizontal curve, and stop sign were found significant only in the male model. On the other hand, variables such as intersection approaches on tangents with flat grade, and driver older than 75 years were found significant only in the female model. In addition, variables such as making turning maneuver, freeway-ramp junction, high speed approach, and so forth were found significant in both models. Estimation findings showed that two parameters in the male model and another two parameters in the female model could be modeled as random parameters, indicating their varying influences on the injury severity due to unobserved effects. In addition to the random parameter logit approach, a deep learning approach based on Artificial Neural Networks was introduced to predict the outcome of the crashes based on 164 variables that are listed in the crash database. The artificial intelligence (AI)-based method achieved an accuracy of 76% indicating the role of the variables in deciding the final outcome. Practical Applications: Future plans are set to study the use of AI on large sized datasets to achieve a relatively high-performance, and hence to be able to identify which variables contribute the most to the final outcome.

Factors affecting fatal PTW at-fault crash outcome metrics at intersections and non-intersections in South Korea

Fatal crashes involving Powered Two-Wheelers (PTWs) are a major health concern in South Korea due to the increase in their usage in the logistics industry. Owing to the way, they are operated on the roadways, most riders end up in fatal crashes. Interestingly, little research exists regarding the impact of risk factors on fatal crashes involving at-fault PTW riders. This study employs a copula-based regression technique to simultaneously model the relationship between crash-risk factors and crash outcome metrics of fatal PTW rider-at-fault crashes, namely the number of crash casualties (casualty size) and the number of vehicles involved in a crash (crash size) at intersections and non-intersection segments. The proposed method was superior compared to the SEM-based bivariate regression approach, and the estimation results showed that there exists a positive relationship between both outcome variables. From the analysis, it was identified that while "other violations" comprising speeding and wrongful overtaking had varying effects on crash size outcomes at the intersection and non-intersection segments, variables such as daytime, winter, head-on collisions, and pedestrian involvement had positive impact on the crash consequence metrics irrespective of the crash location. Insights drawn from the study are used in recommending appropriate countermeasures for improving PTW safety.

A data-driven Bayesian network for probabilistic crash risk assessment of individual driver with traffic violation and crash records

In recent years, individual drivers‘ crash risk assessments have received much attention for identifying high-risk drivers. To this end, we propose a probabilistic assessment method of crash risks with a reproducible long-term dataset (i.e., traffic violations, license, and crash records). In developing this method, we used 7.75 million violations and crashes of 5.5 million individual drivers in Seoul, South Korea, from June 2013 to June 2017 (four years). The stochastic process of the Bayesian network (BN), whose structure is optimized by tabu-search, successfully evaluates individual drivers' crash and violation probability. In addition, the cluster analysis classifies drivers into five distinctive groups according to their estimated violation and crash probabilities. As a result, this study found that the estimated average crash rate within a cluster converges with the actual crash rate by the proposed framework without privacy issues. We also confirm that violation records and expected crash probability are strongly correlated, and there is a direct relationship between a driver’s previous violations and crash record and the future at-fault crash. The proposed assessment method is valuable in developing proactive driver education programs and safety countermeasures, including adjusting the penalty system and developing user-based insurance by recognizing dangerous drivers and identifying their properties.

Risky behavior analysis for cross-border drivers: A logit model and qualitative comparative analysis of odds of fault and injury vulnerability in Guangdong, Hong Kong and Macau

Introduction: Due to globalization and the acceleration of cross-border exchanges, cross-border risk behaviors have received widespread attention. Previous research has concluded that foreign cross-border drivers engage in relatively more risk-taking behavior patterns and are likely to experience a higher crash rate or be more inclined to cause severe crashes. However, there is little evidence on the comparison of drivers who belong to the same ethnic group driving across within-country borders. Method: Based on the cross-border motor-vehicle crash reports in 2006–2010 from the Road Traffic Accident Database of the China Ministry of Public Security, this paper examines the risk factors of being at fault and getting killed or seriously injured in cross-border traffic crashes and casual paths toward crash liability and injury severity for Hong Kong and Macao drivers driving in the Chinese mainland. Results: There are extremely complex factors behind drivers from Hong Kong and Macao causing at-fault crashes or sustaining fatal and serious injuries in the Chinese mainland. Factors such as gender, age, illumination, and weather conditions do not individually affect the risk of driver at-fault crashes or severe casualties in the crashes among Hong Kong and Macao drivers driving in the Chinese mainland. Nonetheless, collectively, these factors influence them along with different vehicle types, roads, and environmental factors. Conclusions: This paper provides more theoretical findings for understanding the compound effect of multiple risk factors involving cross-border at-fault crashes or serious casualties. The conclusions of this research are valuable as representative references for cross-border risk management policies. Practical applications: To reduce the effects of different factors on cross-border risky driving behaviors and/or injurious crashes, various measures should be focused on, including specialized driver training, enhancement of the roads/environment, development of effective road safety campaigns, and directives facilitating cross-border cooperation in the field of road safety.

Mental health conditions and unsafe driving behaviors: A naturalistic driving study on ADHD and depression

Introduction: Road injuries remain a persistent public health concern across the world. The task of driving is complicated by mental health conditions, which may affect drivers’ executive functioning and cognitive resource allocation. This study examines whether attention-deficit/hyperactivity disorder (ADHD) and depression are associated with unsafe driving behaviors. Method: Generalized linear mixed models were employed to estimate the association of self-reported ADHD and depression with 18 unsafe driving behavior types found prior to at-fault crashes and near-crashes using a large-scale naturalistic driving dataset. Driver demographics, cognitive traits, environmental factors, and driver random effects were included to reduce confounding and biases. Results: Controlling for other covariates, people with self-reported ADHD were more likely to have performed improper braking or stopping (OR = 4.89, 95% CI 1.82–13.17) prior to an at-fault crash or near-crash, while those with self-reported depression did not have a significant association with any unsafe driving behavior. Conclusions: After accounting for demographic, cognitive, and environmental covariates, individuals with ADHD and depression were not prone to purposefully aggressive or reckless driving. Instead, drivers with self-reported ADHD may unintentionally execute unsafe driving behaviors in particular driving scenarios that require a high level of cognitive judgment. Practical Applications: These findings can inform the curriculum design of driver’s education programs that help learners with mental health conditions gain practice in certain road scenarios, for example, more practice on preemptively reducing speed instead of making sudden brakes and smooth turning on curved roads for students with ADHD. Furthermore, specific advanced driver assistance systems may prove particularly helpful for drivers with ADHD, such as detection of leading objects and curve speed warning.

How are different sources of distraction associated with at-fault crashes among drivers of different age gender groups?

IntroductionDistracted driving has been well researched, however the comparison between different age-gender groups on the impact of distracted driving has not been explored. Most crash analysis research does not distinguish driver responsibility, so the role that distractions has in at-fault crashes is unknown. Without distinguishing at-fault crashes from all-cause crashes, distracted driving's detrimental effects could be underestimated. ObjectiveThis study aims to systematically assess the risk of at-fault crashes associated with different sources of distraction among six groups by driver age (Teens 16–19, Adults 20–64, Seniors 65+) and gender. MethodsCrashes where a study participant was deemed at fault were identified using human expert annotated variables from the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study dataset. Generalized linear mixed models were performed to assess the adjusted odds ratios of 10 distraction types associated with the at-fault crashes while controlling for environmental factors. ResultsThe main findings are (1) The highest contributing distraction types in at-fault crashes were In-Cabin Objects, Mobile Device, External Scenes, and In-Vehicle Information Systems (IVIS) as indicated by their influence on multiple age-gender groups and the magnitude of odds ratios; (2) Teens and adults were more distraction-prone than seniors, although seniors had the greatest at-fault crash risks associated with In-Cabin Objects, Mobile Device, and IVIS; (3) Distractions impacted females and males similarly; (4) At-fault crashes were more likely to have the significant distraction types present than all-cause crashes. ConclusionThis study adds to the limited literature on at-fault crashes particularly as it explores the role of driver demographics and distracted driving. Analyzing the risks of distracted driving by age-gender group shows that specific activities can be riskier for a certain population. The effects of distractions may be overlooked without fault determination. Distractions by external scenes, in-vehicle technologies, and in-cabin objects should not be overlooked, in addition to mobile device use.

In-depth analysis of crash contributing factors and potential ADAS interventions among at-risk drivers using the SHRP 2 naturalistic driving study

Objective Motor vehicle crashes remain a significant problem. Advanced driver assistance systems (ADAS) have the potential to reduce crash incidence and severity, but their optimization requires a comprehensive understanding of driver-specific errors and environmental hazards in real-world crash scenarios. Therefore, the objectives of this study were to quantify contributing factors using the Strategic Highway Research Program 2 (SHRP 2) Naturalistic Driving Study (NDS), identify potential ADAS interventions, and make suggestions to optimize ADAS for real-world crash scenarios. Methods A subset of the SHRP 2 NDS consisting of at-fault crashes (n = 369) among teens (16–19 yrs), young adults (20–24 yrs), adults (35–54 yrs) and older adults (70+ yrs) were reviewed to identify contributing factors and potential ADAS interventions. Contributing factors were classified according to National Motor Vehicle Crash Causation Survey pre-crash assessment variable elements. A single critical factor was selected among the contributing factors for each crash. Case reviews with a multidisciplinary panel of industry experts were conducted to develop suggestions for ADAS optimization. Critical factors were compared across at-risk driving groups, gender, and incident type using chi-square statistics and multinomial logistic regression. Results Driver error was the critical factor in 94% of crashes. Recognition error (56%), including internal distraction and inadequate surveillance, was the most common driver error sub-type. Teens and young adults exhibited greater decision errors compared to older adults (p < 0.01). Older adults exhibited greater performance errors (p < 0.05) compared to teens and young adults. Automatic emergency braking (AEB) had the greatest potential to mitigate crashes (48%), followed by vehicle-to-vehicle communication (38%) and driver monitoring (24%). ADAS suggestions for optimization included (1) implementing adaptive forward collision warning, AEB, high-speed warning, and curve-speed warning to account for road surface conditions (2) ensuring detection of nonstandard road objects, (3) vehicle-to-vehicle communication alerting drivers to cross-traffic, (4) vehicle-to-infrastructure communication alerting drivers to the presence of pedestrians in crosswalks, and (5) optimizing lane keeping assist for end-departures and pedal confusion. Conclusions These data provide stakeholders with a comprehensive understanding of critical factors among at-risk drivers as well as suggestions for ADAS improvements based on naturalistic data. Such data can be used to optimize ADAS for driver-specific errors and help develop more robust vehicle test procedures.

What Are the Restraint Practices, Preferences, and Experiences When Australian Parents Travel with Their Children in a Rideshare Vehicle?

This study aimed to explore the preferences, experiences and restraint practices of Australian parents travelling with their children in rideshare vehicles. Six hundred and thirty-one participants completed an online survey (M = 39.2 years, SD = 10.5, Range = 18.0–70.0 years; Female: 63.4%). Most participants (59.1%) reported that they had not travelled in a rideshare vehicle with their youngest child (M = 7.2 years, SD = 5.2, Range = 0.0–17.0 years; Male: 54.2%). Participants who reported that they have travelled with their youngest child in a rideshare vehicle tended to: be younger, identify as male, have completed an Undergraduate or Postgraduate degree, reside in the Australian Capital Territory, earning a higher yearly household income, and were involved in an at-fault crash in the past two years. In addition, these participants were: less likely to have a ‘younger’ youngest child, less likely to ‘always’ wear a seatbelt while travelling in their private motor vehicle, and also less likely to ‘always’ restrain their child in an appropriate restraint while travelling in their private motor vehicle. Prohibitive reasons for not travelling in a rideshare vehicle included: cost (29.3%), concerns over driver safety (27.5%), concerns over travelling with children in a rideshare service (24.8%), or inconvenience (24.3%). Participants who reported that they had travelled in a rideshare vehicle with their youngest child reported lower rates of appropriate restraint use within the rideshare vehicle (57.3%) than when travelling in their private motor vehicle (85.6%). Reasons associated with inappropriate restraint use within the rideshare vehicle included: unavailability of a child restraint (39.6%), travelling a short distance (33.0%), were not required to use one in this situation (33.0%), or the parent did not have a restraint with them (26.4%). Given the increasing popularity of rideshare services in Australia, and globally, the urgent adaption of rideshare-specific policy, legislation, education, and design in relation to child restraint requirements is needed to ensure the safety of child occupants.

The rider behavior questionnaire to explore associations of motorcycle taxi crashes in Cartagena (Colombia)

Objective This study aimed to identify the association between behavioral factors and crashes of motorcycle taxi riders using the Motorcycle Rider Behavior Questionnaire (MRBQ). Methods This study was a cross-sectional survey of motorcycle taxi riders in Cartagena. The MRBQ was adapted to the socio-cultural context and contained 45 items. The survey was conducted between February 2019 and May 2019. The items of the MRBQ were processed using factor analysis. Four logistic regression models were used to analyze the association between behavioral factors and aspects of demographics, operating conditions, and experiencing near-crashes, crashes, traffic tickets, and at-fault crashes. Results Four hundred and thirty-eight motorcyclists participated. The exploratory factor analysis extracted five elements: stunts, speed violations, traffic errors, control errors, and safety, explaining 42% of the variance. The increase in riding per week showed significant differences with stunts, speed violations, and traffic errors. Riding experiences, traffic errors, control errors, and safety were significantly associated with crashes and near-crashes. Stunts were the strongest factors related to traffic tickets. Speed violations were the strongest factors associated with at-fault accidents. Conclusions The study showed recent results considering behavioral, exposure, and operational conditions in a group of motorcycle taxi riders. The study recommends some practical implications for the well-being of motorcyclists and road safety.

Driving specialist's ratings of on‐road performance and naturalistic driving crashes and near‐crashes

This study aimed to evaluate the association between a Certified Driving Rehabilitation Specialist's (CDRS) ratings of on-road driving performance by older drivers and at-fault crash and near-crash involvement using naturalistic driving techniques where crashes and near-crashes are recorded in everyday driving through in-vehicle instrumentation. This is a cohort study of 144 drivers aged 70 years and over who were recruited due to a recent ophthalmology clinic visit at the University of Alabama at Birmingham. Baseline measurements consisted of demographics, visual status, and other health variables. At-fault crashes and near-crashes over 6 months were identified through instrumentation placed in their personal vehicle that recorded vehicle kinematics and video. After 6 months, a CDRS completed an on-road assessment and provided a composite rating on specific driving behaviors and a global score. Rate ratios examining the association between older drivers with worse CDRS composite scores and rates of at-fault crashes, at-fault near-crashes, and combined at-fault crashes and near-crashes were significantly higher compared to drivers with better scores. Results were similar for the CDRS global score. Motor vehicle administrations use CDRS ratings to make decisions about licensure, and in clinical programs such as those based at rehabilitation clinics use them to make recommendations about fitness to drive and rehabilitation. This study suggests that these decisions and recommendations are valid from a safety standpoint.

Indicators of Crash Risk in Older Adults: A Longitudinal Analysis From the ACTIVE Study.

Objective: This study aims to examine indicators of crash risk longitudinally in older adults (n = 486). Method: This study applied secondary data analyses of the 10years of follow-up for the ACTIVE study combined with state-recorded crash records from five of the six participating sites. Cox proportional hazards models were first used to examine the effect of each variable of interest at baseline after controlling for miles driven and then to assess the three cognitive composites as predictors of time to at-fault crash in covariate-adjusted models. Results: Older age, male sex, and site location were each predictive of higher crash risk. Additionally, worse scores on the speed of processing cognitive composite were associated with higher crash risk. Discussion: Results support previous findings that both age and male sex are associated with higher crash risk. Our significant finding of site location could be attributed to the population density of our testing sites and transportation availability.

Naturalistic Driving Techniques and Association of Visual Risk Factors With At-Fault Crashes and Near Crashes by Older Drivers With Vision Impairment

Government motor vehicle crash reports used in the study of driver safety can be biased and incomplete. Naturalistic driving methods using in-vehicle instrumentation have been developed in recent years to objectively measure crashes and near crashes as they occur on the road using video and vehicle kinematic data. To examine visual risk factors associated with at-fault crashes and near crashes among older drivers, most of whom have age-related eye conditions associated with vision impairment. This prospective cohort study was conducted at an academic ophthalmology clinic from January 1, 2015, to November 10, 2018, among 154 adults 70 years of age or older who were legally licensed in Alabama and who reported currently driving at least 4 days per week; 137 of 151 participants (90.7%) had an age-related eye condition in at least 1 eye. Drivers participated in a baseline visual function assessment followed by installation of a data acquisition system recording multichannel synchronized video and vehicle kinematics in their personal vehicle. Drivers were instructed to drive for 6 months as they normally would during everyday life. Statistical analysis was performed from June 15 to September 15, 2020. The rate of combined incident at-fault crashes and near crashes, defined by the number of events and the number of miles driven. The sample consisted of 154 drivers (85 men [55.2%]; mean [SD] age, 79.3 [5.1] years). Visual functions associated with crash and near-crash involvement included impaired contrast sensitivity (rate ratio [RR], 2.7; 95% CI, 1.3-5.5), moderate (RR, 2.3; 95% CI, 1.1-4.9) and severe (RR, 5.0; 95% CI, 2.2-11.7) slowing in visual processing speed, and elevated motion perception thresholds for a drifting grating (RR, 1.9; 95% CI, 1.1-3.5). Those with impaired peripheral visual field sensitivity had increased rates of crashes and near crashes (RR, 1.8; 95% CI, 1.0-3.3); however, this finding was not statistically significant (P = .07). With the use of naturalistic driving methods in which crashes and near crashes involving older drivers are objectively measured as they occur on the road, associations have been identified between impaired contrast sensitivity, slowed visual processing speed, and impaired motion perception and an increased rate of a combined total of at-fault crashes and near crashes.

Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data

Traffic violations and improper driving are behaviors that primarily contribute to traffic crashes. This study aimed to develop effective approaches for predicting at-fault crash driver frequency using only city-level traffic enforcement predictors. A fusion deep learning approach combining a convolution neural network (CNN) and gated recurrent units (GRU) was developed to compare predictive performance with one econometric approach, two machine learning approaches, and another deep learning approach. The performance comparison was conducted for (1) at-fault crash driver frequency prediction tasks and (2) city-level crash risk prediction tasks. The proposed CNN-GRU achieved remarkable prediction accuracy and outperformed other approaches, while the other approaches also exhibited excellent performances. The results suggest that effective prediction approaches and appropriate traffic safety measures can be developed by considering both crash frequency and crash risk prediction tasks. In addition, the accumulated local effects (ALE) plot was utilized to investigate the contribution of each traffic enforcement activity on traffic safety in a scenario of multicollinearity among predictors. The ALE plot illustrated a complex nonlinear relationship between traffic enforcement predictors and the response variable. These findings can facilitate the development of traffic safety measures and serve as a good foundation for further investigations and utilization of traffic violation data.

A systematic review and meta-analysis of older driver interventions

The purpose of this systematic review and meta-analysis was to summarize and quantify the effects of different driving interventions among older adults on outcomes of crashes, on-road driving performance, self-reported outcomes of errors and crashes, and driving simulator performance. Randomized controlled trials examining the effects of a driving intervention among older adults ≥ 50 years of age were included. Thirty-one studies were identified using a systematic literature review, and 26 were included in meta-analyses. The following types of driving interventions were identified: physical retraining/exercise (e.g., flexibility and coordination training); visual-perceptual training (e.g., improving figure-ground discrimination); cognitive training (e.g., Useful Field of View cognitive training); education (e.g., classroom driver refresher course); context-specific training (i.e., on-road training in car, driving simulator training); combined intervention approaches (e.g., education and context-specific training combined). Effect sizes were calculated for each driving intervention type relative to control groups using random-effects. Physical retraining/exercise, visual-perceptual training, and combined intervention approaches demonstrated medium to large effects on on-road driving performance, ds = 0.564–1.061, ps < .050. Cognitive training approaches reduced at-fault crashes by almost 30 %, OR = 0.729, 95 % CI [0.553, 0.962], p = .026. Education and context-specific approaches were not efficacious to improve driving safety outcomes, ps> .050. In summary, skill-specific interventions (physical retraining/exercise, visual-perceptual training, cognitive training) and combined intervention approaches improved on-road driving performance and reduced at-fault crashes. Optimizing interventions that target age-related functional declines and combined intervention approaches is recommended.

Association of Photopic and Mesopic Contrast Sensitivity in older drivers with risk of motor vehicle collision using naturalistic driving data

BackgroundOlder drivers have a crash rate nearly equal to that of young drivers whose crash rate is the highest among all age groups. Contrast sensitivity impairment is common in older adults. The purpose of this study is to examine whether parameters from the photopic and mesopic contrast sensitivity functions (CSF) are associated with incident motor vehicle crash involvement by older drivers.MethodsThis study utilized data from older drivers (ages ≥60 years) who participated in the Strategic Highway Research Program Naturalistic Driving Study, a prospective, population-based study. At baseline participants underwent photopic and mesopic contrast sensitivity testing for targets from 1.5–18 cycles per degree. Model fitting generated area under the log CSF (AULCSF) and peak log sensitivity. Participant vehicles were instrumented with sensors that captured continuous driving data when the vehicle was operating (accelerometers, global positioning system, forward radar, 4-channel video). They participated for 1–2 years. Crashes were coded from the video and other data streams by trained analysts.ResultsThe photopic analysis was based on 844 drivers, and the mesopic on 854 drivers. Photopic AULCSF and peak log contrast sensitivity were not associated with crash rate, whether defined as all crashes or at-fault crashes only (all p > 0.05). Mesopic AULCSF and peak log sensitivity were associated with an increased crash rate when considered for all crashes (rate ratio (RR): 1.36, 95% CI: 1.06–1.72; RR: 1.28, 95% CI: 1.01–1.63, respectively) and at-fault crashes only (RR: 1.50, 95% CI: 1.16–1.93; RR: 1.38, 95% CI: 1.07–1.78, respectively).ConclusionsResults suggest that photopic contrast sensitivity testing may not help us understand future crash risk at the older-driver population level. Results highlight a previously unappreciated association between older adults’ mesopic contrast sensitivity deficits and crash involvement regardless of the time of day. Given the wide variability of light levels encountered in both day and night driving, mesopic vision tests, with their reliance on both cone and rod vision, may be a more comprehensive assessment of the visual system’s ability to process the roadway environment.